Please do not cite. The complete paper is available at: http://faculty.chicagogsb.edu/erik.hurst/research/
Deconstructing Lifecycle Expenditure
Mark Aguiar
University of Rochester
and
Erik Hurst
University of Chicago
Prepared for the 10th Annual Joint Conference of the Retirement Research Consortium
“Determinants of Retirement Security”
August 7-8, 2008
Washington, D.C.
The research reported herein was pursuant to a grant from the U.S. Social Security
Administration (SSA) funded as part of the Retirement Research Consortium (RRC).
The findings and conclusions expressed are solely those of the authors and do not
represent the views of SSA, any agency of the Federal Government, or the RRC. We
thank Jesse Shapiro for early conversations, which encouraged us to write this paper, as
well as Eric French and Randy Wright for detailed comments. We also thank seminar
participants at the University of Rochester, the NBER Macro Perspectives Summer
Institute Session, the Institute for Fiscal Studies, the Federal Reserve Board of
Governors, Wisconsin, Harvard, Yale, MIT, Chicago, CREI, Stanford, UCLA, MRRC,
the University of Albany, and the PIER/IGIER conference on inequality in
macroeconomics.
Abstract
In this paper we revisit two well-known facts regarding lifecycle expenditures. The first
is the familiar “hump” shaped lifecycle profile of nondurable expenditures. The second
is that cross-household consumption inequality increases steadily throughout the life
cycle.
We document that the behavior of total nondurables masks surprising
heterogeneity in the lifecycle profile of individual sub-components. We find that three
categories account for nearly the entire decline in mean expenditure post-middle age:
food, transportation, and clothing/personal care. All other nondurable categories we
study, including housing services, utilities, entertainment, domestic services, charitable
giving, gambling, etc., show no decline over the life cycle. Similarly, nearly all of the
increase in cross-sectional inequality is driven by these same three categories. Excluding
food, clothing, and transportation from our measure of non durable expenditures reduces
the increase in consumption inequality by a factor of 8, and removes nearly all of the
increase post middle age. We provide evidence that the categories driving life cycle
consumption are either inputs into market work (clothing and transportation) or are
amenable to home production (food). Changes in the opportunity cost of time will cause
movements in expenditure on such goods even if there is no change to lifetime resources.
The patterns documented in the paper suggest that prior inferences from consumption
data regarding the extent of uninsurable risk faced by households are sensitive to the
inclusion of work related expenses and the home production of food. The disaggregated
consumption data also pose a challenge to models that emphasize inter-temporal
substitution or movements in income, including standard models of precautionary
savings, myopia, and limited commitment, to explain the lifecycle profile of
expenditures.
1.
Introduction
This paper reconsiders two prominent features of life cycle consumption
expenditures. The first is the fact that expenditures are “hump” shaped over the life
cycle, peaking in middle age and then declining steadily throughout the second half of the
life cycle. 1 The second fact is that cross-sectional consumption inequality increases
steadily as individuals age. 2 Both facts have had tremendous influence on economists’
inferences about household preferences, the income process that households face, and the
extent to which public and private insurance markets limit household exposure to risk.
In this paper we revisit these two familiar facts by disaggregating nondurable
expenditures into more detailed consumption categories.
We show that there is
substantial heterogeneity across consumption goods with respect to both the life cycle
profile of mean expenditures and the evolution of the cross household variance in
expenditures.
Specifically, we first replicate the standard finding that composite nondurable
expenditures, controlling for family composition, peak in middle age at a level roughly
30 percent higher than expenditures at 25 or 65. Similarly, we document that the crosssectional variance in log nondurable expenditure doubles between ages 25 and 75.
However, we then document that the decline in nondurable expenditure post-middle age
is essentially driven by three categories – food away from home, nondurable
transportation, and clothing/personal care. Moreover, these three categories account for
1
This literature documenting the hump shaped profile of expenditures is large and extends back nearly 40 years. See,
for example, Thurow (1969), Heckman (1974), Carroll and Summers (1991), Attanasio and Weber (1995), Attanasio et
al (1999), Angeletos et al (2001), Gourinchas and Parker (2002), and Fernandez-Villaverde and Krueger (2007). The
hump shape holds for nondurable expenditures as well as total expenditures and persists after accounting for changes in
family size.
2
See Deaton and Paxson (1994), Attanasio and Jappelli (2002), Storesletten et al (2004b), Heathcote et al (2005), and
Guvenen (2007).
1
nearly the entire increase in the cross-sectional variance of expenditures over the
lifecycle. All the other components of our composite nondurable measure (housing
services, utilities, entertainment, domestic services, charitable giving, etc.) show no
decline in expenditures after the age of 45 and exhibit little, if any, increase in cross
sectional variance over the life cycle.
Canonical models of consumption emphasize movements in uninsurable
permanent income as key to both the “hump” shape and the increase in cross-sectional
dispersion. 3
Models based solely on fluctuations in financial resources predict that
categories with larger income elasticities should display greater increases in crosssectional dispersion and more pronounced hump shapes. However, the disaggregated
data show no such pattern. For example, households increase spending on relative
luxuries such as entertainment, charitable giving, and gambling after middle age while
simultaneously reducing spending on food, clothing, and transportation. Similarly, the
cross-sectional dispersion in the former categories all show declines over the life cycle.
The data do, however, support a prominent role for expenses that are closely
linked to a households’ opportunity cost of time. These categories consist of clothing and
transportation, which have been shown to be inputs into market labor supply, as well as
food, which is amenable to home production. As the opportunity cost of time falls over
the lifecycle and, consequently, some households reduce their attachment to the labor
force, expenditures on work related and home produced goods should fall, even if there is
no change in lifetime resources or preferences. Such work related and food expenses
account for the entire decline in nondurable expenditures after middle age, coincident
3
See the discussion in Section 5 for details.
2
with the peak in market labor supply for the average household.
Moreover, while
inequality in composite nondurables increases throughout the life cycle, doubling
between age 25 and 75, inequality in nondurable expenditure excluding food and work
related expenses increases by only 25 percent, with nearly all of the increase occurring
prior to the age of 46 or after the age of 68.
To gain more insight into the importance of work status and work related
expenses we perform a number of additional exercises. We document that the decline in
expenditure on food away from home after middle age is associated with a decline in the
frequency with which individuals frequent fast food establishments or cafeterias, with no
indication that individuals reduce their patronage of restaurants with table service. This
is consistent with food away from home being a work-related expense, particularly as it
relates to movements over the life cycle. Similarly, time diaries indicate that there is a
large decline in time spent commuting to work after the age of 50. However, non-work
traveling increases slightly over the lifecycle.
To the extent that transportation
expenditures are proportional to transportation time, these results imply that the decline
in transportation expenses is due entirely to a decline in work related transportation. We
also document that controlling for labor supply mitigates the decline in spending on
clothes, transportation, and food away from home.
The patterns documented in this paper argue for a reassessment of the mapping of
consumption to uninsurable permanent income. In particular, the patterns of “core”
nondurable expenditures, excluding work related/home production expenses, suggests
that the increase in cross-household consumption inequality increases much less than
suggested by total nondurables and is essentially constant for households between the
3
ages of 45 and 65.
This suggests that households face less uninsurable income risk –
particularly later in life - than suggested by total consumption expenditures. In this sense,
this paper complements recent studies that conclude the canonical consumption models
have overestimated the extent of uninsurable income risk later in the life cycle. 4
2.
Data and Empirical Methodology
A.
Data
To examine the life cycle profile of expenditure and the life cycle evolution of the
cross sectional dispersion, we use data from the Consumer Expenditure Survey (CEX).
Specifically, we use the NBER CEX extracts, which includes all waves from 1980
through 2003. We restrict the sample to households who report expenditures in all four
quarters of the survey and sum the four responses to calculate an annual expenditure
measure.
We also restrict the sample to households that record a non-zero annual
expenditure on six key sub-components of the consumption basket: food, entertainment,
transportation, clothing and personal care, utilities, and housing/rent.
This latter
condition is not overly restrictive, resulting in the exclusion of less than ten percent of the
households. Lastly, we focus our analysis on households where the head is between the
ages of 25 and 75 (inclusive). After imposing these restrictions, our analysis sample
contains 53,412 households.
Appendix A contains additional details about the
construction of the dataset and sample selection.
When examining the life cycle profile of mean expenditures and cross sectional
dispersion, we limit our analysis to nondurables excluding health and education
4
Examples from diverse fields and using different methodology include Keane and Wolpin (1997), Cunha, Heckman,
and Navarro (2005), Guvenen (2007), and Huggett, Ventura, and Yaron (2007).
4
expenditures. Our measure of nondurables consists of expenditure on food (both home
and away), alcohol, tobacco, clothes and personal care, utilities, domestic services,
nondurable transportation, airfare, nondurable entertainment, gambling, business services
and charitable giving. 5
We also examine a broader measure of nondurables which
includes housing services where housing services are calculated as either rent paid (for
renters) or the self-reported rental equivalent of the respondent’s house (for home
owners). We exclude expenditures on education and health care from the analysis, as the
utility (or returns) from consuming these goods vary significantly over the life cycle.
Likewise, we exclude all durables aside from housing given the difficulty in creating
annual service flow measures for these expenditures.
Our measure of nondurable expenditure plus housing services comprises roughly
70 percent of household annual monetary outlays. The remaining portion of annual
outlays can be attributed to expenditures on durables such as automobiles, home
furnishing, and large entertainment durables (16 percent); health expenditures (6
percent); education expenditures (2 percent); and other expenditures which are difficult to
classify (5 percent). 6
5
Examples of expenditures that are included in each of the expenditure categories can be found in the data appendix
and the corresponding documentation to the NBER CEX files.
6
These other categories include, among others, life insurance premiums, interest paid on consumer credit, college
dormitory fees, money allocated to burial plots, union dues, books, lodging expenses away from home, legal services,
etc. Some of these categories were excluded because of the classification system introduced by Sabelhaus and Harris
when creating the NBER CEX files. For example, the category of “books” includes money spent on books for leisure
reading and books purchased for course work. Likewise, the category of “other lodging expenditures” includes both
college dormitory expenses as well as vacation rentals. For consistency, we excluded from our analysis any category
that combined some health or education component. However, in the NBER working paper version of this paper, we
examined these categories in greater detail. None of our results are changed if we included these measures in our
nondurable expenditure measure. This is not surprising given that they comprise only a small fraction of total
household expenditures.
5
B.
Estimating the Life Cycle Profile of Expenditure
When examining life cycle profiles of mean expenditure and cross sectional
dispersion, we adjust all expenditures for cohort and family composition effects. The
CEX is a cross-sectional survey and therefore age variation within a single wave
represents a mixture of life cycle and cohort effects.
Moreover, expenditures are
measured at the household level and not the individual level. Household size has a hump
shape over the life cycle, primarily resulting from the fact that children enter and then
leave the household. Likewise, children of differing ages have different consumption
needs. Additionally, marriage and death probabilities change over the life cycle. We
identify life cycle from cohort variation by using the multiple cross-sections in our
sample, and use cross-sectional differences in family composition to identify family
composition effects.
Formally, to estimate the life cycle profile of expenditures, we estimate the
following regression:
ln(Citk ) = β 0 + β age Ageit + β cCohortit + β fs Familyit + ε itk
(1)
where Citk is expenditure of household i during year t on consumption category k, Ageit is
a vector of 50 one-year age dummies (for ages 26-75) referring to the age of the
household head 7 , Cohortit is a vector including eleven five-year age of birth cohort
dummies, and Familyit is a vector of family structure dummies that include a maritalstatus dummy, 10 household size dummies, and controls for both the number and age of
7
For married households, we use the husband’s age. See the data appendix for additional details of how we identify
household head in multi-adult households.
6
household children aged 21 or under. Specifically, we control for the number and age of
household children by including dummy variables for the number of children in the
following age categories: 0-2, 3-5, 6-13, 14-17, and 18-21. Moreover, for the latter two
categories, we create separate indicators for male and female children. Our detailed
family composition controls allow us to control flexibly for the potential that children of
different ages and sex have different consumption needs or preferences. Moreover, the
fact these family composition effects are allowed to differ across expenditure categories
accommodates varying degrees of returns to scale across goods. 8
The coefficients on the age dummies, βage, represent the impact of the life cycle
conditional on cohort and family size fixed effects, both of which we allow to vary across
expenditure categories. Each of these age coefficients should be interpreted as log
deviations from the spending of 25 year olds.
As is well known, co-linearity prevents the inclusion of a vector of time dummies
in our estimation of (1). To account for changes in the relative price of each consumption
category, we deflate all categories into constant dollars using the relevant CPI productlevel deflator, if available.
Otherwise, we use the relevant PCE deflator from the
National Income Accounts. 9 All data in the paper are expressed in 2000 dollars. Any
movements in expenditure patterns over time that are not captured by the five-year cohort
dummies or by the price deflators will be interpreted as variation over the life cycle. In
Appendix B, we discuss in detail the robustness exercises we performed to assess
whether our results are sensitive to our choice of including cohort effects as opposed to
8
Because of concerns about a potential correlation between family composition and permanent income, we performed
many robustness exercises where we control for family composition in different ways. See the robustness appendix
posted online at www.markaguiar.com/aguiarhurst/lifecycle/robustness_appendix.pdf for details of the robustness
exercises. In summary, our results are robust to all of these alternative specifications.
9
We have verified our results are robust to using a common aggregate deflator (CPI-U) for all categories..
7
time effects. The main conclusions of our paper hold whether we choose to estimate life
cycle profiles including cohort effects or year effects.
In Section 4, we augment our benchmark estimates by controlling explicitly for
relative prices and total expenditure. We postpone discussion of that methodology until
Section 4.
C.
Estimating the Life Cycle Profile of Cross Sectional Expenditure Dispersion
To estimate the life cycle profile of the cross sectional expenditure dispersion, we
start by computing (σ 2 )itk , the variance of ε itk (the residuals from (1)) for each age and
cohort. We then estimate the following equation:
k
k
(σ 2 )itk = α 0k + α age
Ageit + α cohort
Cohortit + ηitk .
(2)
k
The vector of age coefficients, α age
, for each consumption category, k, provides our
estimates for the evolution of cross sectional variance in expenditures over the life cycle.
This method is essentially the same as the one used by Deaton and Paxon (1994). The
age coefficients should be interpreted as being deviations from the variance of log
expenditure observed for 25 year olds. As with mean expenditures, collinearity prevents
the identification of time trends separate from age and cohort effects.10
We, however,
show that the main results of our paper with respect to the changing cross sectional
variance of expenditures over the life cycle are invariant to the inclusion of time effects
(as opposed to cohort effects) in Appendix B.
10
See Heathcote et al (2005) for a detailed sensitivity analysis regarding cohort versus time fixed effects in identifying
the evolution of inequality over the life cycle.
8
3.
Life Cycle Expenditure Patterns by Category
In this section, we document the existence of substantial heterogeneity across
different consumption categories with respect to both the life cycle profile of expenditure
and the evolution of the cross sectional variance of expenditure over the life cycle.
For context, we first show the trends in life cycle expenditure and life cycle
dispersion for our composite nondurable measures. These results are shown in Figures 1a
(life cycle expenditure profile) and Figure 1b (life cycle profile of cross sectional
expenditure variance).
The solid line in each figure represents the results using
nondurable expenditures without housing services. The dotted line represents the results
using nondurable expenditures with housing services.
Figure 1a replicates the well-documented profile of nondurable expenditures over
the life cycle, with nondurable expenditures excluding housing services peaking in
middle age at roughly 25 percent (that is, 0.25 log points) higher than the level of 25 year
old expenditure, and then declining by nearly 30 percent over the latter half of the life
cycle. Nondurable expenditures inclusive of housing services rises faster early in the life
cycle, but then does not decline as significantly later in the life cycle. The gap between
the two series represents the life cycle behavior in housing services, which we will
discuss on its own in the next sub-section. 11 These results are consistent with a large
literature documenting the hump shaped profile of nondurable expenditures over the life
cycle. 12
11
Yang (2008) documents that the life cycle profile of housing services is different from the life cycle profile of
composite nondurable expenditures. She then writes down a model of where housing consumption is costly to
downsize to explain the differences between housing and nondurable expenditures. As we show below, the life cycle
profile of housing looks like the life cycle profile of most non durable consumption categories.
12
See, for example, Attanasio et al (1999) and Fernandez-Villaverde and Krueger (2007) and the cites within.
9
Figure 1b shows the increase over the life cycle of the cross-sectional variance of
log nondurable expenditures relative to the variance observed for 25 year olds. The
variance for nondurable expenditures with and without housing expenditures for 25 year
olds is 0.16 and 0.17, respectively. Between the ages of 25 and 75, the cross sectional
variance of nondurable expenditures increase by roughly 20 points, regardless of whether
or not housing services are included in the measure of nondurable expenditures. These
magnitudes are similar to the results reported by Guvenen (2007) and are consistent with
the findings of others that the cross sectional variance of expenditure increases by at least
100 percent over the life cycle. Additionally, most of the increase comes later in the life
cycle (after the age of 40) leading some researchers to conclude that there is a prominent
role for permanent income shocks later in the life cycle.
A.
Life Cycle Profiles of Disaggregated Expenditures
In Figures 2a and 2b, we plot the life cycle expenditure profiles for the sub-
components of our composite nondurable. Specifically, we document the life cycle
spending patterns separately for housing services, utilities, nondurable entertainment,
nondurable transportation, food consumed at home, food consumed away from home,
alcohol and tobacco, domestic services, clothing and personal care, and a residual “other”
category. The “other” nondurable category includes airfare spending, charitable giving,
and net gambling receipts.
All category specific life cycle spending profiles are
estimated using (1) and, as a result, are adjusted for cohort and family composition.
Table 1 reports the share of spending out of total nondurable expenditures (both with and
without housing) for each of the consumption subcategories.
10
For expositional purposes, we group the categories by whether or not they decline
after middle age. In particular, Figure 2a depicts categories that show no decline over the
life cycle, while Figure 2b collects those categories that exhibit declines after middle age.
This categorization underscores that not all categories share the prominent “hump” seen
in composite nondurables.
As reported in Table 1, the non-decreasing categories
constitute 54 percent of nondurable expenditures including housing services.
We begin our discussion of Figure 2a with nondurable entertainment spending.
Nondurable entertainment consists of such expenditures as cable subscriptions, movie
and theatre tickets, country club dues, pet services, etc. It does not include durable
expenditures such as television sets and does not include reading material and magazine
subscriptions. The average annual expenditure on entertainment totals $1,260 in year
2000 dollars and accounts for 7 percent of non-durable expenditure excluding housing
services (Table 1). Entertainment expenditures increase until the early 40s (by roughly
70 percent).
However, expenditures on entertainment do not fall after middle age.
Instead, spending on entertainment remains roughly constant between 45 and 59 and then
increases by an additional 8 percent between the ages of 60 and 68.
As seen in Figure 2a, housing services, utilities, domestic services, and other
nondurables exhibit similar life cycle profiles to that displayed by entertainment. All
these categories increase significantly between the age of 25 and the age of 44, continue
to increase up through the age of 59, and then increase sharply after the age of 60.
The continuous increases in categories depicted in Figure 2a begs the question of
what categories drive the decline in composite nondurable consumption spending after
middle age. Figure 2b answers this question. Specifically, food at home spending
11
increases 24 percent between 25 and 44, declines by 7 percent between 45 and 59, and
then declines another 4 percent between 60 and 68.
The middle age declines in
expenditures are even larger for transportation (22 percent between 45 and 59), clothing
and personal care (39 percent between 45 and 59), and food away from home (62 percent
between 45 and 59).
These four categories comprise 42 percent of nondurable
expenditures with housing services.
The final “declining” category is alcohol and tobacco, which is not included in
Figure 2 but is included in Table 1. This category behaves in a manner distinct from the
other categories depicted in Figures 2a and 2b. Alcohol and tobacco expenditure falls
continuously over the entire life cycle. Moreover, the decline in expenditure is very
large: Spending on alcohol and tobacco falls by 1.22 log points between 25 and 44,
another 1.65 log points between 45 and 59, and another 1.03 log points between 60 and
68.
Even though alcohol and tobacco comprises only 4 percent of composite
nondurables, its large decline contributes significantly to the overall decline in
nondurable spending after middle age.
Table 1 summarizes the patterns shown in Figures 2a and 2b. It should be noted
that expenditures on all subcategories displayed in Figures 2a and 2b increase over the
front half of the life cycle. The difference between the two groups of categories occurs
after the mid-40s. Models that predict declines in spending on all consumption goods
after middle age (like standard models incorporating household impatience or poor
planning) are inconsistent with the disaggregated spending data.
The data reported in Table 1 and Figure 2 also suggest a re-interpretation of the
so-called “retirement consumption puzzle,” which refers to the decline in expenditures
12
observed around retirement. In particular, the declines at retirement should be placed in
the context of the broader trend of declining life cycle expenditures after middle age.13
The categories that exhibit declining expenditures during the peak retirement years (6068) are the same categories that exhibit declining expenditures over the second half of the
life cycle (after the age of 45). Specifically, declines in expenditure around retirement
are observed only for food, clothing/personal care, transportation, and alcohol and
tobacco. 14 There is no evidence that entertainment, housing services, utilities, domestic
services, charitable giving, or airline travel declines during the peak retirement years.
Taken together, the results cast doubt on the existence of a generic decline in expenditure
for all consumption categories around the time of retirement.
3b.
Lifecycle Profile of Cross Sectional Dispersion for Disaggregated Categories
The life cycle profiles of mean expenditures by category are mirrored in the life
cycle profiles of cross-sectional dispersion. As a general rule, the categories for which
expenditure does not decline past middle age are the same categories for which there is
no substantial increase in cross-sectional dispersion.
Conversely, the categories for
which expenditures decline over the latter half of the life cycle are the same categories
for which the cross sectional dispersion increases dramatically over the life cycle. These
results are shown in Figures 3a and 3b and are summarized in Table 2. As with Figure
1b, the age coefficients represent the change in the cross-sectional variance of log
expenditure at each age relative to that of 25 year olds (see equation (2)). For reference,
Table 2 also includes the variance of log consumption at age 25 for each consumption
13
See Hurst (2007) for a survey of the retirement consumption literature
The fact that declines in expenditures at the time of retirement are limited to food, clothing, and non-durable
transportation has also been emphasized by Battistin et al (2006) and Hurst (2007).
14
13
category. As seen in the table, spending on food at home, housing services, and clothing
exhibit rather low cross sectional variances, while alcohol and tobacco, domestic
services, and other nondurables exhibit substantial cross sectional variance.
In Figure 3a, we plot the life cycle profile of the cross sectional variance of log
expenditure for goods that do not experience any increase in the cross sectional variance
over the life cycle. The goods that display no increase in variance are essentially the
same goods that do not decline over the back side of the life cycle. The one difference
between the categories in Figures 2a and 3a is food at home, which is not included in the
former but is included in the latter. Expenditures on food at home do not exhibit an
increasing cross sectional variance over the entire life cycle, although the variance does
increase slightly after the age of 45. The other categories for which inequality does not
increase over the life cycle are housing services, utilities, entertainment, and other
nondurable expenditures.
Figure 3b reveals which categories drive the increasing cross sectional variance of
log expenditures over the life cycle. These categories include nondurable transportation,
clothing and personal care, food away from home, and domestic services. From the
figure and the upper panel of Table 2, we see that at the lower end, the variance of log
transportation expenditure increases by 54 percent (from 0.70 to 1.08). At the upper end,
the variance of log expenditures on food away from home more than doubles, increasing
from 1.54 to 3.45.
Standard models that focus exclusively on shocks to income predict that goods
with ahigh income elasticity should experience the largest changes in both means and
cross-sectional variances. Table 2 and Figures 3 suggest no such pattern. Relative
14
luxuries like entertainment, gambling, charitable giving, and airfare, together with such
basics as housing and utilities, show no similarity to the life cycle pattern of composite
nondurables. Other than the contribution of the idiosyncratic category of alcohol and
tobacco, the prominent features of the composite nondurable category are driven by food,
transportation, and clothing/personal care.
These latter categories are perhaps best
considered as inputs into market work (or, in the case of food, amenable to home
production), rather than categories with relatively large income elasticities. In the next
section, we explore this premise in more detail.
4.
The Importance of Food, Clothing and Transportation in Explaining Life
Cycle Profiles
As seen from the discussion in Section 3, there is substantial heterogeneity across
consumption categories with respect to both the life cycle profile of mean expenditures
and the life cycle profiles of the cross-sectional variance. Leaving aside alcohol and
tobacco spending, spending on food (particularly food away from home), clothing and
personal care, and transportation seem to be driving both the decline in nondurable
spending after middle age and the increase in the cross sectional variance of log nun
durable spending over the life cycle.
One potential reason why these categories may behave differently over the life
cycle is that food is amenable to home production and clothing and transportation
spending are complements with market work. To the extent that the opportunity cost of
time evolves over the life cycle, one would predict changes in spending to occur within
15
these categories given a standard model of household optimization augmented with a
home produced good and work related expenses.
Appendix Figure A-1a shows the life cycle profile of the labor supply of
household heads from the Consumer Expenditure Survey. Our analysis sample for this
exercise is identical to the sample used above to document the life cycle consumption
profiles. We show two measures of labor supply – the fraction of heads working (solid
line) and the normal hours per week worked by the head (dotted line). This latter
measure is not conditioned on working. Both labor force measures start to decline for
individuals in their early 50s.
Referring back to Figure 1a, this is the time that
nondurable expenditures begin to decline. Appendix Figure A-1b shows the variance of
the two labor force measures over the life cycle. The variance of work hours start to
increase sharply starting for individuals in their early fifties. Given the decline in work
hours starting for individuals after middle age, it is not surprising to find that work related
expenditures should start to decline. Likewise, given the increase in the variance of labor
force participation starting in the early 50s, it is not surprising to see the variance of work
related expenditures increase between the ages of 50 and 65.
In this section, we discuss evidence that food is amenable to home production and
that clothing and transportation are complements with market work. We then divide
nondurables into three sub-aggregates – food, work related expenses, and all other
nondurable expenditures – and document their respective means and variances over the
life cycle. Finally, we show that controlling for work status mitigates the decline in work
related spending over the latter half of the life cycle.
16
A.
The Home Production of Food
In Aguiar and Hurst (2005, 2007b), we have explored the differences between
food expenditures and food intake. Using data from the Continuing Survey of Food
Intake of Individuals (CSFII) which measures food intake at the individual level using
detailed food diaries (including the quality of food consumed), Aguiar and Hurst (2005)
shows that food intake does not decline over the life cycle despite the decline in
expenditures after middle age. On the contrary, using the detailed data on the quantity
and quality of food consumed, they find that food intake actually increases after middle
age. 15
Aguiar and Hurst (2007b) estimate a model of home production and food
shopping to explain the differences between food expenditures and food intake. Using
data from the American Time Use Survey (ATUS), which measures the amount of time
individuals spend preparing meals and shopping for food, and data from the Nielson
Homescan Panel, which measures the prices that households pay for a given food good
(measured at the level of the universal product code), Aguiar and Hurst (2007b) finds that
after middle age individuals allocate more time to preparing meals and shopping for food,
and as a result, pay lower prices for constant quality consumption goods. Their estimated
model of food production and food shopping matches the decline in food at home
spending and food away spending. Moreover, like the actual data on food intake, their
estimated model predicts rising food intake over the life cycle.
Figure 4 sheds additional light on the margins of substitution that takes place with
respect to food spending over the life cycle. Using data from the Continuing Survey of
15
This can be seen from Appendix Figure A1 of the NBER working paper version of Aguiar and Hurst (2005).
17
Food Intake of Individuals (CSFII), we measure an individual’s propensity to eat away
from home at various types of eating establishments. The primary design of the CSFII is
to measure food intake via food diaries. 16
At any time food was consumed, the
respondents were asked to provide very detailed comments about what they consumed,
when they consumed it, and where they purchased it. We construct a variable called
“eating away from home” which takes the value of 1 if the respondent reported
purchasing food at a restaurant with table service, a restaurant without table service (i.e.,
establishments like fast food chains), a cafeteria, or a bar/tavern. Respondents in the
CSFII spend roughly 2.5 days in the sample. For the entire sample, 64 percent of
individuals eat away from home at least once during their time in the sample. 17
Of
those, 38 percent eat at fast food establishments, 33 percent eat at restaurants with table
service, 10 percent eat at cafeterias, and 6 percent eat at bars. The percentages sum to
more than 64 percent given some individuals eat at multiple establishments during their
time in the sample.
Figure 4 depicts the propensity to eat at the various types of restaurants. As with
the expenditure data, we adjust the propensity to eat away from home for changing
family composition and all comparisons are made relative to households in their late 20s
(25-29).
The overall pattern is similar to expenditures on food away from home,
especially as it relate to the declines after middle age. In particular, the propensity to eat
16
The CSFII is a large nationally representative survey of individuals (as opposed to households). As in Aguiar and
Hurst (2005), we use the surveys waves conducted in 19xx-xx and 19xx-xx for our analysis. For our analysis, we
restricted the sample to 25-75 year olds. Our total sample size used for the results in Figure 4 was 6,615 individuals.
See the data appendix for a more detailed description of the CSFII data.
17
This number is approximately consistent with data from the 2003 American Time Use Survey that shows that for a
similar sample, 26 percent of individuals eat at a restaurant, fast food establishment or bar on any given day. If eating
away from home is i.i.d., this implies that for a 2.5 day interval, 53 percent of individuals would report time spent in a
restaurant, fast food establishment or bar.
18
away from home falls by nearly 28 percentage points for individuals in their late 60s
relative to individuals in their early 20s. However, the entire decline is due to a declining
propensity to eat at fast food restaurants and cafeterias.
There is no decline in the
propensity for individuals to eat at restaurants with table service as they age. This finding
is consistent with the premise that the decline in food expenditures reflects households
switching towards home production as their opportunity cost declines past middle age.
The shift toward home production of food results in households purchasing fewer meals
from fast food and cafeterias, which are close substitutes to home-produced food.
B.
Transportation and Clothing As Work Related Expenses
Spending on clothing and transportation has long been viewed as complements
with market work. For example, Nelson (1989) and DeWeese and Norton (1991) both
use data from the Consumer Expenditure Survey to show that even after controlling for
total income, total expenditures, and family composition, married couples spend much
more on clothing when both spouses are working than otherwise similar households in
which only the husband worked. Both papers conclude that work status directly effects
household spending on clothing.
Similarly, when analyzing consumption decisions of retirees, Banks et al (1998)
explicitly refer to spending on canteen and restaurant meals, transportation, and adult
clothing as “work-related expenditures”. A similar classification was employed recently
by Battistin et al (2006) when examining consumption changes around retirement in
Italy.
19
Lastly, there is a literature on computing welfare calculations across people that
advocates netting out work related expenses from measured income.
For example,
Lazear and Michael (1980) argued that, “There are certain costs of employment, such as
costs of transportation to work, requisite clothing expenditures for employed persons,
union or professional fees, etc. These work-related costs should be netted out of income
in obtaining an indicator of standard of living”. 18
Transportation expenditures reflect the need to commute to work as well as travel
for other purposes. While expenditure data does not separately measure costs due to
work travel from non-work travel, we use time diaries from the pooled 2003-2005
American Time Use Survey (ATUS).to gauge the relative importance of each.19
The
detailed categories of the ATUS allow us to separately identify time spent traveling to
and from work from time spent traveling for other reasons (including going to grocery
store, going to visit friends, going to the movies, etc.). The average individual between
the ages of 25 and 75 spends 9.0 hours per week traveling, with 2.3 hours per week being
traveling associated with commuting to/from work. For those that work, work related
travel represents roughly one-third of all time spent traveling.
Figure 5 shows the life cycle profile of travel time after adjusting for changing
family composition. The family composition controls include a marital status dummy,
dummies for household size, and a dummy for whether the household has a child under
the age of 5.
The life cycle profile is expressed as hours per week deviation from
households aged 25-29. Consistent with the decline in transportation expenditures over
18
See Lazear and Michael, page 203.
The ATUS is a nationally representative survey which uses time diaries to measure how individuals allocate their
day. For a detailed account of the ATUS, see Aguiar and Hurst 2007a. For this analysis, we restrict the sample to only
households between the ages of 25 and 75. Our total sample size was 38,876 individuals. See the data appendix for
additional details about the ATUS, our sample selection, and our definition of variables.
19
20
the life cycle starting for households in their early 50s, the decline in transportation time
also starts for individuals in their early 50s. However, as seen from Figure 5, the entire
decline in travel time occurs due to a decline in traveling to and from work. Non-work
travel time actually increases over the life cycle.
The data from the time use surveys
suggests that the decline in transportation spending over the life cycle stems from the
decline in time spent commuting to work.
Again, this is consistent with the fact that
transportation expenditures, and particularly their fluctuations over the life cycle, have a
substantial work related component.
C.
Work Hours and Lifecycle Consumption Profiles
The previous discussion and the patterns depicted in figures 2 and 3 suggest a
natural decomposition of nondurable expenditures. Namely, we consider three subaggregates:
(i) work related expenses, consisting of clothing/personal care and
transportation; (ii) food, which includes food consumed at and away from home; and (iii)
all other expenditure categories including housing services but excluding tobacco and
alcohol. We refer to this latter measure as “core nondurable” expenditures. This 3-way
decomposition simplifies the analysis while retaining the key patterns of the more
disaggregated data.
The mean and cross-sectional variances of these categories are depicted in Figures
6a and 6b, respectively.
Table 3 summarizes the life cycle profiles for the three
composite consumption goods. In Figure 6a, we see that core nondurables increases
sharply up through middle age and then increases slightly thereafter.
work related expenditures fall sharply after middle age.
21
Both food and
A striking reflection of our results on consumption inequality is clear in Figure
6b. The cross sectional variance of core nondurable expenditures displays a dramatically
different life cycle pattern than does the cross sectional variance of total nondurable
expenditures as analyzed by Deaton and Paxson and others, and replicated in Figure 1b
above. In particular, up through the age of 68, the cross sectional dispersion in core
nondurables increases by only .05 points, with nearly all of the increase coming prior to
the age of 45. Given that the variance of core nondurables for 25 year olds is 0.28, the
cross sectional dispersion of core nondurables increases by less than 18 percent over the
life cycle. This is an order of magnitude lower than the increase in cross sectional
variance for total nondurables.
The implication is that essentially all of the increase in cross-sectional variance
over the life cycle stems from work related expenses and food, with the increase in food
inequality primarily reflecting food away from home. The sharp increase in inequality in
expenditure on work related expenses is clear in Figure 6b. Note in particular that the
variance of work related expenses increases significantly after middle age, while core
nondurables shows no comparable increase.
The cross-sectional variance of total
nondurables increases by nearly 10 percentage points between the ages of 46 and 68
(Figure 1b), which represents nearly two-thirds of the increase in life cycle dispersion of
total nondurables. All of the increase in variance between the ages of 45 and 68 in total
nondurables is due to an increase in the variance of work related expenditures and food
(as well as the covariances). The increase in dispersion of work related expenses reflects
the fact that the cross-sectional variance in labor supply increases after middle age, as
well (see Figure A-1b). This suggests that uninsurable shocks to permanent income are
22
not a major concern between the ages of 40 and 68. There is an increase in variance after
the age of 68 in core nondurables, which is unlikely to reflect earning risk but perhaps
reflects late-in-life health shocks. 20
Given the importance of work related expenses in driving changes in expenditure
over the life cycle, a natural approach would be to control for work status. A difficulty
with simply adding controls for employment status is the fact that labor supply is closely
associated with permanent income. For example, lower wage workers tend to work
fewer hours than high wage workers (see Aguiar and Hurst (2007a) and Vandenbroucke
(2007)). Absent a panel, controls for labor supply will also proxy for permanent income.
However, we can accommodate this for consumption sub-categories by including
nondurable expenditures as an additional control.
Specifically, we estimate the following for each of our three sub-aggregates:
sitk = ω0 + ωage Ageit + ωc Cohortit + ω fs Familyit + ∑ ω pk ln Pt k +ω p ln Pt
k
+ ω X ln X it + ωL Lit + ε ,
(3)
k
it
where sitk is the share of consumption category k out of total nondurable expenditures
excluding tobacco and alcohol, where k equals work related, food, and core nondurables
expenditures. These shares sum to one. The age, cohort, and family status controls are
the same as in equation (1). We include as additional controls the log price index of each
of our sub-aggregates (lnPk) as well as the overall price index (lnP). We compute the
category specific price indices by using the weighted share of the price indices for the
goods that comprise the consumption category. We include log expenditure on total
20
The Appendix shows a series of robustness exercises for Figures 6a and 6b. First, we show that if we estimate the
specifications plotted in 6a and 6b with year effects as opposed to cohort effects, the conclusions are unchanged (see
Appendix B). Also, we show that the patterns are the same for low educated households as they are for high educated
households (see Appendix C).
23
nondurables with housing services (ln X) excluding alcohol and tobacco, which is also
just the sum of work related, food, and core nondurable expenditures.
Finally, we include a vector of dummies describing the household labor supply
(L). Our work status controls include: a vector of 7 dummies indicating the number of
weeks worked by husbands during the previous year, a vector of 7 dummies indicating
the number of weeks worked by the wife during the previous year, a vector of 9 dummies
indicating the number of hours usually worked during the week by the head, and a vector
of 9 dummies indicating the number of hours usually worked during the week by the
wife. We found that labor supply has a nonlinear effect on consumption, and so include a
very rich set of work status controls. To avoid issues of changes in household formation
and its effect on labor supply, we restrict our analysis sample to include only married
households. This leaves us with a sample of 32,204 households.
Given the fact that expenditures on the individual consumption categories are
determined simultaneously and the fact that any measurement error in one category will
lead to measurement in total nondurables, we follow the standard practice of
instrumenting total expenditure with log total household family income and education
dummies. Our measure of total household family income includes labor, asset, and
transfer income of both husbands and wives.
Note that equation (3) is essentially the almost ideal demand system (AIDS) of
Deaton and Muelbauer (1980), conditioned on work status, family size, and age. We
impose the restriction that the overall price index is given by the CPI-U, but do not
impose restrictions related to consumer optimization such as symmetry and homogeneity.
The inclusion of work status controls to form a conditional demand system follows the
24
important work of Browning and Meghir (1991) and Blundell et al (1994), whose work
we discuss in Section 5.
The results from the estimation are shown in Figures 7a (work related expenses),
7b (food), and 7c (core nondurables). In each figure we depict the age coefficients from
(3) (dotted line), as well as the age coefficients when we omit work status controls (solid
line). As always, each point represents the deviation from age 25, with the units for
Figure 7 being share of total nondurable expenditures. As seen in 7a, and consistent with
the results in 6a, the share of total expenditures allocated to work related expenses
declines over the second half of the life cycle. Specifically, the share decreases by 5
percentage points between the ages of 50 and 68.
However, after accounting for
household labor supply, the decline is reduced by nearly half.
While adjusting for labor supply substantially mitigates the decline in work
related expenses, it does not explain the entire decline. When we disaggregate the work
related expenditures into its two components and re-estimate (3) on the disaggregated
categories, we find that work status controls explain almost all the decline in
transportation expenditures and less than half the decline in clothing expenditures (results
not depicted).
There are several reasons why clothing behaves differently than
transportation. The first is that clothing is semi-durable, unlike the other categories of
nondurable consumption. Therefore, there may be a wedge between the service flow of
clothing and measured expenditures.
Moreover, there may be changes in required
clothing expenses over the life cycle depending on occupation and career trajectories.
Therefore, labor supply will absorb only some of the life cycle variation in work related
clothing expenses. Finally, Charles et al (2008) have argued that clothing expenditures
25
are a status good. As the need to signal status becomes less important over the life cycle
(as individuals have already found a spouse, gotten a job, and made friends etc.), the use
of clothing as a symbol of status would diminish.
We also estimated (3) restricting our sample to households between the ages of 25
and 50 and including a dummy variable indicating whether the household head is
working (and omitting all other work status controls).
This captures the effect of
employment for individuals in prime working years. The coefficient is large and positive
on the working dummy for both clothing and transportation.
In other words, all else
equal, households spend a larger share of their consumption on clothing and
transportation when the household head is working (even after controlling for total
expenditures).
Figures 7b and 7c show the similar patterns for food at home, food away from
home, and core nondurables. As with work related expenses, both food at home and food
away from home decline over the latter half of the life cycle. Including work status
controls explains nearly the entire decline in food away from home. The share spent on
food at home actually declines more when work status controls are included. This
reflects that food away from home is more sensitive to work status, so in terms of shares,
households substitute away from restaurants towards food at home as they stop working.
Figure 7 and the results in this section generally, support the premise that the
categories that drive the aggregate life cycle profiles for means and variances – clothing,
transportation, and food – are sensitive to work status.
Equation (3) can only be
estimated for shares given the inability to observe permanent income absent a panel.
However, the behavior of shares can be mapped into total expenditures using a structural
26
model, something we are pursuing in ongoing research. What is clear directly from the
data is that work related goods are behind the decline in total expenditures and the
increase in inequality after the age of 50.
5.
Discussion and Related Literature
There is a large body of work that tries to explain the lifecycle profile of
composite nondurable expenditures, without addressing the heterogeneity found in
disaggregated consumption categories. For example, some authors have argued that the
lifecycle profile represents evidence against the forward-looking consumption
“smoothing” behavior implied by permanent income models, particularly since the hump
in expenditures tracks the hump in labor income (as documented by Carroll and Summers
(1991)). This view interprets expenditure declines in the latter half of the lifecycle as
evidence of poor planning. A related literature has developed which also emphasizes
imperfect household planning based on the sharp decline in expenditures at the onset of
retirement (see, for example, Bernheim, Skinner, and Weinberg 2001). Models of limited
commitment to plans (such as Angeletos et al 2001) share the implication that the decline
in expenditures late in life is due to insufficient resources. Standard models of poor
planning or dynamic inconsistency, however, do not predict that households late in the
life cycle reduce some expenditures while simultaneously increasing others.
Another literature has combined rational, forward looking agents with incomplete
markets. In particular, the hump shaped profile in expenditure reflects optimal behavior
if households face liquidity constraints combined with a need to self-insure against
idiosyncratic income risks (see, for example, Zeldes 1989, Deaton 1991, Carroll 1997,
27
Gourinchas and Parker 2002). Households build up a buffer stock of assets early in the
lifecycle, generating the increasing expenditure profile found during the first half of the
lifecycle. The decline in the latter half of the lifecycle is then attributed to impatience
coming to the fore, once households accumulate a sufficient stock of precautionary
savings. Similarly, the increase in cross-sectional dispersion of total expenditures has
been interpreted as evidence of large, uninsurable idiosyncratic shocks to income.
Such precautionary savings models have been extremely influential, in part due to
their ability to explain the prominent features of lifecycle expenditures in a rational agent,
incomplete markets framework. Indeed, several important studies have used expenditure
profiles to “back out” or verify measures of labor income risk over the lifecycle (see, for
example, Deaton and Paxson (1994) as well as more recent papers by Storesletten et al
(2004a, 2004b) and Guvenen (2007)).
A related literature uses movements in
consumption to infer movements in permanent income (see, for example, Blundell and
Preston (1998) and Aguiar and Gopinath (2007)).
Of course, the quality of these
measures of income risk depends crucially on the validity of the underlying model of
consumption.
Precautionary savings models also have strong predictions for the lifecycle
behavior of goods with different income elasticities. The standard precautionary savings
model works off the tension between the need to accumulate assets for insurance versus
impatience relative to the market interest rate. A fairly high degree of impatience is
necessary to explain the sharp decline in expenditures in the latter half of the lifecycle. 21
21
The important role impatience plays in these models is highlighted by the fact that Gourinchas and Parker (2002) are
able to obtain a very precise estimate of time preference. As discussed in that paper (p. 73), this reflects that fact that
the precautionary savings model’s predictions are extremely sensitive to the discount rate.
28
However, if impatience is the predominant force driving the decline in expenditures over
the second half of the lifecycle, then categories of consumption for which there is a high
degree of inter-temporal elasticity should decline faster than those with a low degree of
substitutability. Given the equivalence between inter-temporal elasticity and income
elasticity (see Browning and Crossley 2000), this implies that luxury goods (such as
entertainment, gambling, and charity) should decline more in the latter half of the
lifecycle than necessities (such as food and transportation).
Note that both the precautionary saving models and the poor planning models
place an emphasis on income fluctuations.
The poor planning models emphasize
deterministic trends in lifecycle labor income.
The precautionary savings models
emphasize income uncertainty. In particularly, the high degree of impatience in the
precautionary savings model needed to explain the sharp decline in expenditures late in
life must be matched with a commensurately high degree of income uncertainty early in
the lifecycle. This latter component is necessary to explain why agents save and exhibit
an upward profile of expenditure early in the lifecycle, despite the high subjective
discount rate. The tight link between income risk and impatience relative to the interest
rate is also a familiar feature in incomplete market models with infinitely lived agents
(see for example, Huggett 1993 and Aiyagari 1994).
By focusing on the disaggregated data, we documented that the primary
movements of expenditures later in the lifecycle are inconsistent with the models that rely
exclusively on precautionary savings, myopia, or limited commitment. We should stress,
however, that our work does not imply these forces are not at work at all. For example,
the increases early in the life cycle may indeed reflect a precautionary savings motive.
29
However, without accounting for the role that work related expenses play, attempts to use
total expenditure to back out permanent income risk will be mis-specified.
The “non-separability” between consumption and work status has been
documented by, among others, Browning and Meghir (1991) and Blundell et al (1994),
using conditional demand system analysis. Our work complements these studies by
arguing that once we strip out work related expenses, mean nondurable expenditures do
not decline and there is little increase in cross-sectional inequality after middle age.
6.
Conclusion
This paper documented that the hump in lifecycle expenditures and the increase in
consumption inequality over the life cycle is driven primarily by work related expenses
or goods amenable to home production. There is no sense in which goods with a high
income elasticity display more prominent patterns over the life cycle, suggestion a force
other than uninsurable permanent shocks is also at work. We provide evidence that
changing labor supply is a promising candidate.
The facts documented in the paper have important implications for linking
consumption movements to income shocks. In particular, it is important to separate
shocks to resources from movements in labor supply (or demands on time more
generally).
We have documented the relevance of this point for both average
expenditures over the lifecycle as well as the cross sectional dispersion of expenditures.
While this paper focuses on lifecycle movements, the same issue arises in studies of the
business cycle (see, for example, Greenwood, Rogerson, and Wright (1995)). Recessions
are periods in which both income and employment fall. Analyses that ignore the latter
30
will draw misleading conclusions about the importance of income uncertainty and the
extent of insurance.
31
Appendix A: Data
A1.
CEX Data
This paper uses data from the Consumer Expenditure Survey’s quarterly interview
survey.
The survey unit is a household (consumer unit).
Each consumer unit is
interviewed once per quarter for five consecutive quarters. The first interview collects
demographic data and inventories major durables. The subsequent four interviews collect
recall data on expenditures over the preceding three months. We collapse the four
interviews into a single annual observation per household, summing over the quarterly
expenditures. In particular, we do not use the panel dimension of the four quarterly
intervies.
While expenditure is reported at the household level, demographics are reported
for individuals. We use demographic characteristics reported by the household head. A
head is defined as the member who identifies himself or herself as the “head of
household” in the survey. If there are multiple heads, we identify the head as the male (if
one is present) and resolve any remaining ties by employment (employed over
nonemployed), age (eldest), and marital status (married over non-married).22
We use the extracts compiled by Ed Harris and John Sabelhaus and provided by
the NBER (http://www.nber.org/data/ces_cbo.html).
documentation
for
this
paper
can
be
found
All data, programs, and
on
(www.markaguiar.com/aguiarhurst/lifecycle/datapage.html).
the
authors’
website
Harris and Sabelhaus
aggregate expenditures into 47 categories, which are listed in the documentation posted
22
There are a handful of households with multiple heads who share the same sex, age, employment status, and marital
status (as well as household size). However, as these are the only demographic variables used in this paper, this
duplication is immaterial to identifying the demographic characteristics of the household.
32
on the authors’ website. The mapping from our consumption categories to their 47
categories can be found in Appendix Table A1. The Harris and Sabelhaus dataset
includes households whose first interview was conducted between the first quarter of
1980 and the second quarter of 2003. Due to changes in the survey methodology, data
from the last two quarters of 1985 and 1995 are omitted. 23 The data set contains a total
of 167,133 households.
We restrict the Harris and Sabelhaus sample in the following ways. First, we
keep households whose heads are between age 25 and 75. To obtain reliable estimates of
cohort effects, we restrict attention to cohorts with at least 10 years of data. In particular,
we restrict the sample to households whose head is at most 65 in 1980Q1 and at least 35
in 2003Q2.
This leaves 122,962 households.
Second, the household must have
completed all four expenditure surveys, providing a complete picture of annual
expenditures. There are 75,883 such households in the sample, or roughly 62 percent.
Harris and Sabelhaus provide adjusted weights to use with the restricted sample.
However, the restricted sample of Harris and Sabelhaus also excludes households with
incomplete income reports and students. Usage of their adjusted weights necessitates
excluding these households as well, leaving 58,305 households.
Our final sample restriction is that households must have strictly positive
expenditure on six major expenditure categories:
food, housing services, utilities,
clothing and personal care, nondurable transportation, and nondurable entertainment.
Roughly 92 percent of the sample satisfied this last criterion, resulting in a sample of
53,412 households. This is our main sample for analysis.
23
Prior to 1984, only urban consumers were surveyed. Exclusion of these years does not significantly alter
the results reported in the paper.
33
A2.
Data From American Time Use Survey (ATUS)
We use the 2003, 2004, and 2005 waves of the American Time Use Survey
(ATUS) conducted by the U.S. Bureau of Labor Statistics (BLS). Participants in ATUS,
which includes children over the age of 15, are drawn from the existing sample of the
Current Population Survey (CPS). The individual is sampled approximately 3 months
after completion of the final CPS survey. At the time of the ATUS survey, the BLS
updated the respondent’s employment and demographic information. The ATUS waves
totaled 20,720, 13,973, and 13,038 respondents in 2003, 2004, and 2005, respectively.
We restrict our sample to respondents aged 25 through 75, resulting in sample sizes of
16,860, 11,436, and 10,580, respectively. We pool these 38,876 respondents into a single
cross section.
The survey uses a 24-hour recall of the previous day’s activities to record time
diary information. The unit of analysis is an individual, and only one individual per
household is surveyed. We control for effects of marriage and family size by regressing
the amount of time (in levels) for a specific activity on age controls, a dummy for marital
status, and ten family size dummy variables, and report the coefficients on the age
controls.
The ATUS reports time allocation using over 400 detailed activity codes. For our
analysis we focus on three aggregates: total travel time (classification category 17 in
2003 and 2004 classification category 18 in 2005), travel associated with work (sub
category 4 out of total travel time), and all other travel time.
34
A3.
Data from Continuing Survey of Food Intake of Individuals (CSFII)
For the analysis in Table 4, we use data from the Continuing Survey of Food Intakes
by Individuals (CSFII) collected by the U.S. Department of Agriculture. The survey is
cross sectional in design and is administered at the household level. We pool the two
most recent cross sectional surveys; the first interviewed households between 1989 and
1991 (CSFII_89) and the second interviewed households between 1994 and 1996
(CSFII_94).
The CSFII_89 and CSFII_94 were designed to be nationally representative. Based
on sample averages, the demographic coverage of the CSFII closely tracks that of the
PSID. The 1989 data also includes an additional data set that oversamples low income
households. We exclude the oversample from our analysis. When analyzing individuallevel data, we restrict our analysis to household heads.
Each household member in the CSFII data also filled out detailed food diaries,
recording their total food intake during a particular 24-hour period, with the CSFII_89
collecting three days and CSFII_94 two days of diaries, respectively. As part of their
entries, they had to record where their food was purchased. We focus on the food
purchased at non-grocery establishments. In particular, we only examine food purchased
at restaurants with table service (restaurants), restaurants with counter service (fast food
establishments), cafeterias, and bars. Collectively, we refer to these categories as food
purchased away from home.
The data sets track standard economic and demographic characteristics of its survey
respondents including age, educational attainment, race, gender, occupation, employment
status, hours worked, retirement status, family composition, geographic census region,
35
whether the household lives in an urban area, home owner status, and household income.
The survey also asks respondents detailed questions regarding health status, health
knowledge, and preference for nutrition. 24
24
See the Data Appendix of Aguiar and Hurst (2004) for a detailed discussion of the CSFII survey methodology and a
comparison of the sample demographics in the CSFII to the sample demographics from other large household based
surveys.
36
Appendix B: Year vs. Cohort Effects
All the analysis in text controlled for cohort effects as opposed to time effects.
Controlling for cohort heterogeneity is important when using cross sectional data given
that younger cohorts tend to have higher lifetime resources than older households. If
lifetime resources for successive cohorts increasing, excluding cohort effects will bias
down estimated age effects of older households relative to younger households.
However, there may be trends and high frequency disturbances in consumption patterns
that argue for time effects. Some of this may result from changing relative prices of
different goods over time. However, it may also reflect changing aggregate economic
conditions and changes in preferences or technology.
Given the co-linearity of age, year of birth, and time, we cannot flexibly control
for all three effects. To assess how sensitive our main results are to the choice between
cohort versus time effects, in this section we re-estimate (1) and (2) with year effects
instead of cohort effects for total expenditures as well as the three sub-aggregates
discussed in section 4. The results are shown in Appendix Figures A2 and A3. Figure
A-2a is akin to Figure 1a.
Regardless of whether we estimate (1) with year effects or
cohort effects, lifecycle expenditures are humped shape. Just like with cohort effects, the
estimates with year effects find that lifecycle expenditures increase by 30 percent up
through middle age and then declines by nearly 30 percent though the late 60s. One
difference between Figure 1a and Appendix Figure A-2a is that the hump shape in
nondurable expenditures with year effects in unchanged regardless of whether (dotted
line) or not (solid line) housing services are included.
37
In Appendix Figure A-2b, we see that nearly all the decline in nondurable
expenditures is due to declines in work related expenses (50 percent post middle age)
and, to a lesser extent, food (30 percent post middle age). Core nondurables does decline
slightly post middle age (by about 10 percent), but the decline is much smaller than the
decline in total nondurable expenditures. Moreover, given that we are excluding cohort
effects and there has been an increase in lifetime resources for younger cohorts, it is not
surprising that when we exclude cohort effects we get a slight dip in core nondurable
spending. Nevertheless, the general pattern is similar whether or not we include year
dummies or cohort dummies. Nearly all the decline in nondurable expenditures is driven
by work related expenses and food in both instances.
The results in Appendix Figure A-3a replicate those in Figure 1b.
When we
replace cohort effects with year effects, the cross sectional variance in total nondurables
still increases substantially over the lifecycle. However, the magnitude of the increase is
less with the year effects than the cohort effects. Specifically, with the year effects, the
cross sectional variance increases by only 63 percent (0.10 over a base of 0.16) as
opposed to the 100 percent increase estimated with cohort effects. However, as seen in
Appendix Figure A-3b, the increase in dispersion is driven mostly by work related
expenses. Between the ages of 45 and 65, there is no trend in the cross-sectional
dispersion of core nondurables.
While some of the magnitudes differ, the main conclusion regarding the
importance of distinguishing work related expenses from other nondurables is robust to
whether we control for year effects or cohort effects.
38
Appendix C:
Expenditure Profiles By Education
Are there differences in life cycle profiles of food, work related expenses, and
core nondurables by educational attainment? Figures A-4a and A-4b show similar plots
as Figures 6a and 6b for households where the head has 12 years of schooling or less
while Figures A-5a and A-5b show similar plots for households where the head has more
than 12 years of schooling. It is interesting to note that our key findings hold within each
educational grouping. For both low and high educated households, there is no decline in
core nondurables expenditures after middle age and there is only a little increase in the
cross sectional dispersion of core nondurables over the lifecycle between the ages of 40
and 68.
39
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43
An Empirical
Table 1: Summary of Change in Expenditure over the Lifecycle by Consumption Category
Disaggregated Consumption Group
Share of Non
Durable
Expenditure
Share of Non
Durable
Expenditure with
Housing
Log Change
Between
25 and 44
Log Change
Between
45 and 59
Log Change
Between
60 and 68
Decreasing Categories
Food at Home
Transportation
Clothing and Personal Care
Food Away from Home
Alcohol and Tobacco
0.23
0.21
0.11
0.09
0.06
0.15
0.14
0.07
0.06
0.04
0.24
0.25
0.02
0.09
-1.22
-0.07
-0.22
-0.39
-0.62
-1.65
-0.04
-0.13
-0.18
-0.31
-1.03
0.14
0.07
0.06
0.03
0.34
0.09
0.05
0.04
0.02
0.67
0.72
0.75
1.33
1.46
0.15
0.28
0.00
0.15
0.22
0.10
0.10
0.13
0.08
0.27
0.66
1.00
0.24
0.37
-0.14
-0.05
-0.07
-0.02
Non-Decreasing Categories
Housing Services
Utilities
Entertainment
Other Non-Durable
Domestic Services
Non Durable Expenditure
Non Durable Expenditure w/Housing
1.00
Note: Table summarizes the life cycle profiles of expenditures for different consumption categories as shown in Figures 2a and 2b. See Figures 2a and 2b for sample description
and empirical methodology. In summary, for each category, we regress log expenditures in 2000 dollars on five-year cohort dummies, marital status and detailed family
composition controls, as well as one year age dummies. The log changes are the difference in the coefficients on the age dummies at the respective ages in each column. See text
for definition of each category and the appendix for data sources. For each age, we average over three years, with 25 representing ages 25-27, 45 representing ages 44-46, 60
representing 59-61, and 68 representing ages 66-68. The first two columns show the share of spending on the consumption commodity out of total non durable expenditures
without housing service (column 1) and with housing services (column 2).
44
Table 2: Dispersion of Expenditure over the Lifecycle by Consumption Category
Cross-sectional
Variance of Log
Expenditure at 25
Change
Between
25 and 45
Change
Between
45 and 60
Change
Between
60 and 68
Change
Between
25 and 75
Increasing Categories
Transportation
Clothing and Personal Care
Food Away From Home
Alcohol and Tobacco
Domestic Services
0.70
0.63
1.54
5.80
6.82
-0.14
0.18
0.00
1.53
0.84
0.11
0.53
1.29
2.62
1.15
0.04
0.09
0.42
1.05
0.47
0.38
0.91
1.91
4.82
2.85
Non Increasing Categories
Housing Services
Utilities
Entertainment
Other Non-Durable
Food at Home
0.41
0.89
1.29
9.57
0.41
-0.07
-0.56
-0.31
-0.71
-0.05
-0.12
-0.09
-0.10
-0.91
0.02
-0.07
-0.05
-0.17
-0.27
0.01
-0.27
-0.76
-0.69
-2.39
-0.02
Non Durable Expenditure
Non Durable Expenditure w/Housing
0.17
0.16
0.05
0.05
0.07
0.06
0.02
0.02
0.16
0.16
Disaggregated Consumption Group
Note: Table summarizes the life cycle profiles of expenditures for different consumption categories as shown in Figures 3a and 3b. See Figures 3a and 3b for sample description
and empirical methodology. In summary, for each category, we regress log expenditures in 2000 dollars on five-year cohort dummies, marital status and family size dummies, as
well as one year age dummies. At each age and for each cohort, we compute the standard deviation of the residuals from this regression. The first column is the average standard
deviation for age 25, pooling all cohorts. For the remaining columns, we regress the standard deviation on age and cohort dummies, and use the age dummies to report the lifecycle
profile. For each age, we average over three years, with 25 representing ages 25-27, 45 representing ages 44-46, 60 representing 59-61, and 68 representing ages 66-68.
45
Table 3: Summary of Our Composite Measures of Consumption
a. Life Cycle Expenditure Summary
Disaggregated Consumption Group
Core Nondurables
Total Food Expenditures
Work Related Expenditures
Share of Non
Durable Expenditure Log Change Between Log Change Between Log Change Between
with Housing
25 and 44
45 and 59
60 and 68
0.54
0.24
0.22
0.65
0.16
0.16
0.13
-0.17
-0.25
0.08
-0.09
-0.14
b. Life Cycle Cross Sectional Variance Summary
Disaggregated Consumption Group
Core Nondurables
Total Food Expenditures
Work Related Expenditures
Cross-sectional
Variance of Log
Expenditure at
25
Percentage
Point Change
Between
25 and 44
Percentage
Point Change
Between
45 and 59
Percentage
Point Change
Between
60 and 68
Percentage
Point Change
Between
25 and 75
0.28
0.24
0.34
0.02
0.00
0.05
0.02
0.06
0.15
0.02
0.00
0.04
0.07
0.07
0.30
Note: Table summarizes the life cycle profiles of expenditures for different consumption categories as shown in Figures 6a and 6b. See Figures 6a and 6b for sample description
and empirical methodology.
46
Appendix Table A1: Mapping Consumption Categories
into the Harris and Sabelhaus Classification
Our Category
Food At Home
Food Away From Home
Alcohol and Tobacco
Clothing and Personal Care
Utilities
Domestic Services
Transportation
Entertainment
Other Non Durables
Housing Services
Harris and Sabelhaus Category
Food Off Premise (23)
Food on Premise (24), Food Furnished Employees (25)
Tobacco Products (26), Alcohol Off Premise (27),
Alcohol on Premise (28)
Clothing and Shoes (29), Clothing Services (30),
Jewelry and Watches (31), Toilet Articles and
Preparation (32), and Barbershops, Beauty Parlors, and
Health Clubs (33)
Electricity (38), Gas (39), Water and Other Sanitary
Services (40), Fuel Oil and Coal (41), Telephone and
Telegraph (42)
Domestic Services, Other Household Operations (43)
Tires, Tubes, Accessories, and Other Parts (53),
Repair, Greasing, Washing, Parking, Storage, and
Rental (54), Gasoline and Oil (55), Bridge, Tunnel,
Ferry, and Road Tolls (56), Auto Insurance (57), Mass
Transit Systems (58), Taxicab, Railway, Bus, and
Other Travel Expenses (59)
Recreation Services (64)
Airline Fares for Out of Town Trips (60), Pari-Mutuel
Net Receipts (65), Charitable and Political Giving (69)
See Text For Details
Notes: See online documentation to the NBER CEX files for examples of specific expenditures included in the Harris
and Sabelhaus classification at http://www.nber.org/data/ces_cbo.html.
47
Figure 1a: Non Durable Expenditures Over The Lifecycle,
With (dashed) and Without (solid) Housing Services
0 .4 5
0 .4 0
0 .3 5
0 .3 0
5
2
e
g
A
m
or
F
n
iot
ia
ve
D
g
o
L
0 .2 5
0 .2 0
0 .1 5
0 .1 0
0 .0 5
0 .0 0
25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75
-0 .0 5
-0 .1 0
Notes: This figure plots mean expenditure by age conditional on cohort and family status controls. More specifically,
each point represents the coefficient on the corresponding age dummy from the estimation of equation 1, with age 25
being the omitted group. The sample includes all households where the head is between the ages of 25 and 75 in the
NBER CEX files between the years 1980 and 2003 (53,412 households). All data are weighted to be nationally
representative using the CEX core weights. The lower series (solid) is nondurable expenditures and the upper series
(dashed) is nondurable expenditures plus housing services. See text for definitions of nondurable and housing service
expenditures.
48
Figure 1b: Cross Sectional Variance of Total Non Durable Expenditure Over the
Lifecycle, With (dashed) and Without (solid) Housing Services
0 .2 4
0 .2 0
5 0 .1 6
2
e
g
A
m
o
r
F
n 0 .1 2
o
ti
a
i
v
e
D
g
o 0 .0 8
L
0 .0 4
0 .0 0
25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75
Notes: This figure depicts the life cycle profile of the variance of log expenditure for total nondurables (solid) and total
nondurables with housing services (dashed) conditional on cohort and family composition controls. Specifically, we
compute the standard deviation of the residuals at each age and cohort from the regression of log expenditures on age,
cohort, and family composition controls (equation (1)), and then remove cohort fixed effects from the age-specific
variances (equation (2)). All data are weighted to be nationally representative using the CEX core weights. Sample is
the same as described in the notes to Figure 1a. See text for definitions of nondurable and housing service expenditures.
49
Figure 2a:
Expenditures over the Lifecycle, Categories that Do Not Decline After Middle Age
2.5
2.0
5 1.5
2
e
g
A
m
o
r
F
n 1.0
o
it
ia
v
e
D
g
o 0.5
L
0.0
25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75
-0.5
Entertainment
Utilities
Other Nondurables
Housing Services
Domestic Services
Notes: This figure plots mean expenditure by age for different consumption categories conditional on cohort and
family status controls. More specifically, each point represents the coefficient on the corresponding age dummy from
the estimation of equation 1, with age 25 being the omitted group. The consumption categories we explore in this
figure are Entertainment (filled circles), Utilities (squares), Other Nondurables (diamonds), Housing Services (triangle),
and Domestic Services (open circles). The sample includes all households where the head is between the ages of 25
and 75 in the NBER CEX files between the years 1980 and 2003 (53,412 households). All data are weighted to be
nationally representative using the CEX core weights. See text and Appendix A for a discussion of the consumption
categories.
50
Figure 2b:
Expenditures over the Lifecycle, Categories that Decline After Middle Age
0.4
0.2
0
2 5 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75
5
2 -0.2
e
g
A
m
or -0.4
F
s
n
oi
t -0.6
ai
ve
D
g -0.8
o
L
-1
-1.2
-1.4
A ge
C lothing
Tra nsportatio n
F o o d A t H om e
Food Aw ay From H om e
Notes: This figure plots mean expenditure by age for different consumption categories conditional on cohort and
family status controls. More specifically, each point represents the coefficient on the corresponding age dummy from
the estimation of equation 1, with age 25 being the omitted group. The consumption categories we explore in this
figure are Clothing and Personal Care (filled circles), Transportation (squares), Food at Home (diamonds), and Food
Away from Home (triangle). The sample includes all households where the head is between the ages of 25 and 75 in
the NBER CEX files between the years 1980 and 2003 (53,412 households). All data are weighted to be nationally
representative using the CEX core weights. See text and Appendix A for a discussion of the consumption categories.
51
Figure 3a:
Cross Sectional Variance of Expenditure Over the Lifecycle,
Decreasing Variance Categories
0.5
0.0
5
2
e
g
A-0.5
m
or
F -1.0
se
c
n
er
ef -1.5
fi
D
t
n
i
o -2.0
P
e
g
at
n
ec -2.5
re
P
-3.0
-3.5
25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75
Entertainment
Utilities
Housing Services
Food At Home
Other Non Durable
Notes: This figure depicts the life cycle profile of the variance of log expenditure for different consumption categories
conditional on cohort and family composition controls. Specifically, we compute the standard deviation of the
residuals at each age and cohort from the regression of log expenditure for each category on age, cohort, and family
composition controls (equation (1)), and then remove cohort fixed effects from the age-specific variances (equation
(2)). The consumption categories we explore in this figure are Entertainment (filled circles), Utilities (squares), Other
Nondurables (diamonds), Housing Services (triangle), and Food at Home (open circles). All data are weighted to be
nationally representative using the CEX core weights. Sample is the same as described in the notes to Figure 1a. See
text and Appendix A for a discussion of the consumption categories.
52
Figure 3b:
Cross Sectional Variance of Expenditure Over the Lifecycle,
Increasing Variance Categories
4.0
3.5
3.0
5
2
e
g
A 2.5
m
ro
F
se 2.0
c
n
er
ef 1.5
fi
D
t
in 1.0
o
P
e
g
a
t 0.5
n
ec
re
P
0.0
-0.5 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75
-1.0
Clothing
Transportation
Domestic Services
Food Away From Home
Notes: This figure depicts the life cycle profile of the variance of log expenditure for different consumption categories
conditional on cohort and family composition controls. Specifically, we compute the standard deviation of the
residuals at each age and cohort from the regression of log expenditure for each category on age, cohort, and family
composition controls (equation (1)), and then remove cohort fixed effects from the age-specific variances (equation
(2)). The consumption categories we explore in this figure are Clothing and Personal Care (filled circles),
Transportation (squares), Food at Home (triangles), and Domestic Services (diamond). All data are weighted to be
nationally representative using the CEX core weights. Sample is the same as described in the notes to Figure 1a. See
text and Appendix A for a discussion of the consumption categories.
53
Figure 4: Propensity to Eat Away From Home By Establishment Type
0 .0 5
R esta u ra n ts a t L u n ch
s 0 .0 0
d
l
O
r
a
e-0 .0 5
Y
9
-2
5
2
-0 .1 0
m
o
r
F
n
io
t-0 .1 5
a
i
v
e
D
t-0 .2 0
in
o
P
e
g-0 .2 5
a
t
n
ec
re
P-0 .3 0
R esta u ra n ts a t D in n er
2 5 -2 9
3 0 -3 4
3 5 -3 9
4 0 -4 4
4 5 -4 9
5 0 -5 4
5 5 -5 9
6 0 -6 4
6 5 -6 9
A n y E a tin g
E sta b lish m en t
F a st F o o d a n d
C a f eteria
-0 .3 5
Notes: Data comes from the Continuing Survey of Food Intake of Individuals (CSFII) for the years 1989-1991 and
1994-1996. Figure plots the life cycle profile of the propensity to eat at different types of establishments. “Any Eating
Establishment” refers to the propensity for individuals to eat at a restaurant, a fast food establishment, a cafeteria, or a
bar/tavern during their time in the sample. We decompose restaurants in restaurants visited during lunch time and
restaurants visited during dinner time. To get the age profile, we regress a dummy variable for whether an individual
eats at a given establishment on age caetegories and family composition controls. All life cycle coefficients should be
interpretted as linear probability deviations from 25-29 year olds. See the text for a discussion of the family size
controls. All data are weighted to be nationally representive using the CSFII survey weights. See the text and
Appendix A for additional details of the CSFII sample.
54
Figure 5: Travel Times Over The Life Cycle,
Total Travel Time, Work Travel Time and Non Work Travel Time
1.00
Non Work Travel Time
s
ld0.50
O
r
ae
Y
90.00
25
2
m
or-0.50
F
n
oi
t
ai
ve-1.00
D
k
ee
W
re-1.50
P
sr
u
o-2.00
H
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
65-69
70-74
All Travel Time
Work Travel Time
-2.50
Notes: Data comes from the 2003-2005 American Time Use Sample (ATUS). Figure plots the life cycle profile of the
average time spent “traveling” (in hours per week) adjusted for family composition changes. “All Travel Time” refers to
the amount of time individuals spend travel to/from work (i.e., commuting time) and all other travel time. To get the
age profile, we regress a dummy variable for whether an individual eats at a given establishment on age caetegories and
family composition controls. See the text for a discussion of the family size controls. All age coefficients should be
interpretted as hour per week deviations from 25-29 year olds. All data are weighted to be nationally representive using
the ATUS survey weights. See the text and Appendix A for additional details of the ATUS sample.
55
Figure 6: Summary of Life Cycle Expenditure Patterns
a. Lifecycle Profile of Expenditures
1 .2
1
0 .8
5 0 .6
2
eg
A
m
or 0 .4
F
n
oi
ta
i 0 .2
ve
D
g
o
0
L
25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67
69 71 73 75
-0 .2
-0 .4
-0 .6
C o r e N o n d u rab le
Food
W o r k R ela t ed
b. Lifcycle Profile of Cross Sectional Variance
0 .4 0
0 .3 5
0 .3 0
5
2
eg
A 0 .2 5
m
o
r
F
n
0 .2 0
io
ta
i
v
e
D
t 0 .1 5
in
o
P
eg
0 .1 0
ta
n
ec
re
P 0 .0 5
0 .0 0
25
27 29
31
33 35
37
39
41 43
45
47 49
51
53
55 57
59
61
63 65
67
69 71
73
75
-0 . 0 5
C o r e N o n d u ra b le
Food
W o r k R e la t e d
Notes: Figures 6a are identical to Figures 1a and 1b, respectively, except that we disaggregate non-durable
consumption into only three categories. The categories are food (diamonds) which includes food away from home and
food at home, work related expenses (circles) which include transportation and clothing/personal care, and “core
nondurables” which includes all other categories of total nondurable expenditure (including housing services but
excluding alcohol and tobacco). See the notes to Figures 1a and 1b for additional sample and estimation descriptions.
56
Figure 7: Effect of Work Status on Expenditure Shares:
With (dashed lines) and Without (solid lines) Work Status Controls
a. Work Related Expenses
0
-0 .0 1
s
d
l
O
r-0 .0 2
a
e
Y
5
2-0 .0 3
m
o
r
F
n-0 .0 4
o
ti
a
i
v
e-0 .0 5
D
g
n
i
o-0 .0 6
P
e
g
a
t
n
ec-0 .0 7
re
P
-0 .0 8
-0 .0 9
25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75
b. Food
0 .0 3
s 0 .0 2
ld
O
r
a
e
Y0 .0 1
5
2
m
o
r
F 0
n
io
t
a
i
v
e
D-0 .0 1
g
n
i
o
P
e-0 .0 2
g
a
t
n
ec
re
P-0 .0 3
Food Home
Food Away
-0 .0 4
25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75
57
Figure 7 (continued)
c. Core Non Durables
0 .1 2
s
d
l 0 .1
O
r
a
e
Y
5
2 0 .0 8
m
o
r
F
n
o
ti 0 .0 6
ia
v
e
D
g
n
i
o 0 .0 4
P
e
g
a
t
n
ec
re 0 .0 2
P
0
25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75
Notes: Figure 7 depicts the age coefficients from regression (3) of expenditure shares on age, cohort, family size, price,
and total expenditures, with (dashed) and without (solid) work status controls. Panels A, B and C, depict the share of
expenditure spent on work related goods, food, and core nondurables, respectively.
58
Appendix Figure A-1: Mean and Variance of Work Over the Life Cycle
a. Fraction of Heads Working (Solid) and
Average Head Weekly Work Hours (Dotted)
1
45
0 .9
40
0 .8
35
0 .7
k
ee
W
re
25 P
d
e
k
r
20 o
W
sr
u
15 o
H
30
g
n
i 0 .6
k
r
o
W0 .5
n
oi
tc
ar 0 .4
F
0 .3
10
0 .2
5
0 .1
0
0
25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75
b. Standard Deviation of Fraction of Heads Working (Solid) and
Standard Deviation of Average Head Weekly Work Hours (Dotted)
0 .6 0
25
0 .5 0
k
r
o
W
o
T
0 .4 0
y
its
n
e
p
o
r
P
f
o 0 .3 0
n
o
it
ia
v
e
D
0 .2 0
d
r
a
d
n
a
t
S
0 .1 0
20
15
10
5
0 .0 0
sr
u
o
H
rk
o
W
ly
k
ee
W
f
o
n
o
ti
a
i
v
e
D
d
r
a
d
n
a
t
S
0
25 27 29 31 33 35 37 39 41 43
45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75
Notes: Figures A-1a shows the life cycle profile of the propensity to work (solid line, left axis) and average hours per
week worked (dotted line, right axis) for household heads. The average hours per week series is not conditional on
working. No other controls are used to adjust these series. The sample is identical to the sample described in the Note
to Figure 1a. Figure A-2b shows the corresponding life cycle profile of the standard deviation of the propensity to
work (solid line, left axis) and average weekly work hours (dotted line, right axis) for household heads.
59
Appendix Figure A-2a:
Summary of Lifecycle Profile of Expenditures With Year Effects, Non Durable
Expenditure With (dotted) and Without (solid) Housing Services
0 .3 5
0 .3 0
0 .2 5
s
d
l
O
r
a
e
Y
5
2
m
ro
F
n
o
it
a
i
v
e
D
g
o
L
0 .2 0
0 .1 5
0 .1 0
0 .0 5
0 .0 0
-0 .0 5
-0 .1 0
25
27
29
31
33
35
37
39
41
43
45
47
49
51
53
55
57
59
61
63
65
67
69
71
73
75
73
75
Appendix Figure A-2b:
Summary of Lifecycle Profile of Expenditures With Year Effects
0 .5
0 .4
C o r e N o n D u r a b le s
0 .3
s
d
l
O 0 .2
r
ae
Y
5
2 0 .1
m
or
F
n
0
oi
t
ia
ve
D -0 .1
g
o
L
-0 .2
Food
25
27
29
31
33
35
37
39
41
43
45
47
49
51
53
55
57
59
61
63
65
67
69
71
W o r k R e la te d
-0 .3
-0 .4
Notes: Figures A-2a and A-2b are identical to Figures 1a and 6a, respectively, except that we replaced the cohort
effects with year effects when estimating the life cycle profiles. See the notes to Figures 1a and 6a for additional
sample and estimation descriptions.
60
Appendix Figure A-3a:
Summary of Lifecycle Profile of Cross Sectional Variance With Year Effects
0 .1 2
s 0 .1 0
ld
O
r
ae
Y
5 0 .0 8
2
m
or
F
ec
n
er 0 .0 6
ef
fi
D
t
n
i
o
P 0 .0 4
e
g
at
n
ec
er
P 0 .0 2
0 .0 0
25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75
Appendix Figure A-3b:
Summary of Lifecycle Profile of Cross Sectional Variance With Year Effects,
by Consumption Category
0 .2 5
W o r k R e la te d
s
d
l
O
r
ae
Y
5
2
m
or
F
ec
n
er
ef
fi
D
t
n
i
o
P
e
g
at
en
cr
e
P
0 .2 0
0 .1 5
0 .1 0
C o r e N o n D u r a b le s
0 .0 5
Food
0 .0 0
-0 .0 5
-0 .1 0
25
27
29
31
33
35
37
39
41
43
45
47
49
51
53
55
57
59
61
63
65
67
69
71
73
75
Notes: Figures A-3a and A-3b are identical to Figures 1b and 6b, respectively, except that we replaced the cohort
effects with year effects when estimating the life cycle profiles. See the notes to Figures 1b and 6b for additional
sample and estimation descriptions.
61
Appendix Figure A-4:
Summary of Life Cycle Expenditure Patterns, Low Educated Households
a. Lifecycle Profile of Expenditures
0 .8
0 .6
0 .4
5
2
e
g 0 .2
A
m
or
F
s
0
n
oi
t
25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75
ai
ve
D-0 .2
g
o
L
-0 .4
-0 .6
-0 .8
C o r e N o n d u rab le
Food
W o r k R ela ted
b. Lifcycle Profile of Cross Sectional Variance
0 .3 5
0 .3 0
5 0 .2 5
2
eg
A
m
or 0 . 2 0
F
n
oi
ta
i
ve 0 . 1 5
D
t
in
o
P
eg 0 . 1 0
ta
ne
cr
e 0 .0 5
P
0 .0 0
25
27
29
31
33
35
37
39
41
43
45
47
49
51
53
55
57
59
61
63
65
67
69
71
73
75
-0 . 0 5
C o r e N o n d u ra b le
Food
W o r k R e la t e d
Notes: Figures A-4a and A-4b are identical to Figures 6a and 6b, respectively, except that we replace restricted the
ample to include only households where the head had 12 years of schooling or less. See the notes to Figures 6a and 6b
for additional sample and estimation descriptions.
62
Appendix Figure A-5:
Summary of Life Cycle Expenditure Patterns, High Educated Households
a. Lifecycle Profile of Expenditures
1 .2
1
0 .8
5
2 0 .6
eg
A
m 0 .4
or
F
n
oi
ta
i 0 .2
ev
D
g
o
0
L
25
27
29
31
33
35
37
39
41
43
45
47
49
51
53
55
57
59
61
63
65
67
69
71
73
75
65
67
69
71
73
75
-0 . 2
-0 . 4
-0 . 6
C o r e N o n d u ra b le
Food
W o r k R e la t e d
b. Lifcycle Profile of Cross Sectional Variance
0 .4 0
0 .3 5
5 0 .3 0
2
eg
A
m 0 .2 5
o
r
F
ce
n
er 0 . 2 0
ef
fi
D
t
n
i 0 .1 5
o
P
e
g
at
0 .1 0
en
cr
e
P
0 .0 5
0 .0 0
25
27
29
31
33
35
37
39
41
43
45
47
49
51
53
55
57
59
61
63
-0 .0 5
C o r e N o n d u r a b le
Food
W o r k R e la t e d
Notes: Figures A-5a and A-5b are identical to Figures 6a and 6b, respectively, except that we replace restricted the
ample to include only households where the head had more than 12 years of schooling. See the notes to Figures 6a and
6b for additional sample and estimation descriptions.
63